Relying on Critical Articulators to Estimate Vocal Tract Spectra in an Articulatory-Acoustic Database

(1) Texas A&M University, USA
(2) University of Edinburgh, UK

We present a new phone-dependent feature weighting scheme that can be used to map
articulatory configurations (e.g. EMA) onto vocal tract spectra (e.g. MFCC) through table
lookup. The approach consists of assigning feature weights according to a featureís
ability to predict the acoustic distance between frames. Since an articulatorís predictive
accuracy is phone-dependent (e.g., lip location is a better predictor for bilabial sounds
than for palatal sounds), a unique weight vector is found for each phone. Inspection of
the weights reveals a correspondence with the expected critical articulators for many
phones. The proposed method reduces overall cepstral error by 6% when compared to a
uniform weighting scheme. Vowels show the greatest benefit, though improvements occur for
80% of the tested phones.